Learningtower: Comparative Analysis of PISA 2022 and Historical Data

Shabarish Sai and Guan Ru Chen

Department of Econometrics and Business Statistics

2024-10-18

Contributors

Shabarish Sai Subramanian

Guan Ru, Chen

Dianne Cook

Kevin Y.X. Wang

Priya Ravindra Dingorkar

Introduction

The learningtower R package is designed to streamline the analysis of OECD’s Programme for International Student Assessment (PISA) data. This package provides access to datasets from 2000 to 2022, allowing researchers to explore trends in education, student performance, and other contextual factors. It simplifies the process of handling large, complex datasets, making it easier to conduct comparative studies across countries and years. Currently, we are updating the 2022 version of the learningtower package to ensure compatibility with the latest PISA data and functionalities

Learningtower Package





The Learningtower package provides access to PISA datasets from 2000 to 2022, allowing researchers to explore trends in education, student performance, and other contextual factors.

In Learningtower contain mainly 3 datasets:

  • Student is a dataset of students scores in mathematics, reading and science.
  • School is a dataset of school’s detailed information, i.e. school weight, school funding distribution, private/public sectors, etc.
  • Countrycode is a dataset of a mapping of a country/region’s ISO code to its full name.

What is Programme for International Student Assessment (PISA)?

Global Examination

Measures student performance in reading, math, and science

Target Group

Assesses 15-year-old students’ knowledge and skills

Global Reach

81 OECD member countries, 700,000+ students in 2022

Educational Environment Research

Additional questionnaires done by students, teachers, and school principals to gather contextual data on educational environments, socio-economic status, and more.

PISA Dataset

year country school_id student_id mother_educ father_educ gender computer internet math read science stu_wgt desk room dishwasher television computer_n laptop_n car book wealth escs
2022 ALB 800282 800001 ISCED 1 ISCED 2 female yes NA 180 248 335 3.2 NA NA NA NA NA NA NA 101-200 NA 1.11
2022 ALB 800115 800002 ISCED 1 ISCED 2 male no no 308 258 315 4.3 NA no NA 1 0 0 0 11-25 NA -3.05
2022 ALB 800242 800003 ISCED 3A ISCED 2 male yes yes 268 285 359 7.8 NA yes NA 1 1 0 1 101-200 NA -0.19
2022 ALB 800245 800005 ISCED 1 ISCED 1 female yes yes 273 322 215 8.5 NA yes NA 1 1 0 0 11-25 NA -3.22
2022 ALB 800285 800006 ISCED 3A ISCED 2 female yes yes 435 464 435 3.7 NA yes NA 1 1 1 2 11-25 NA -1.05
2022 ALB 800172 800007 ISCED 3A ISCED 3A male yes yes 534 451 479 4.3 NA no NA NA 1 2 1 more than 500 NA 1.09

Variable Description

From original dataset, We collect the following variables:

  • Year

  • Country

  • School

  • Student information: ID, gender, test scores, student weight

  • Economic factors, such as: Parent’s education level, household belongings(i.e. computer, internet, etc.) as well as constructed index like escs.

Gender Gap Analysis

Math

Reading

Science

World Map

Code 
ggplotly(mrs_maps)

EcoSocio Factors Analysis

Temporal Analysis

Comparison

Explanation for Temporal Analysis

From 2000 to 2022, the three charts show the temporal trends of math, reading, and science student performance scores in various nations. Labels are used to draw attention to certain countries’ performance trends, and each line shows the average score for that nation.

  • Mathematics: Singapore routinely ranks top, whereas Brazil and Peru have lower scores with some positive trends. Around the 500 score point, nations like Belgium, Australia, and Germany continue to perform comparatively steadily.

  • Reading: Australia, Belgium, and Canada continue to do well, while Singapore once again takes the lead. Thailand, Brazil, and Peru perform worse, though they gradually become better.

  • Science: Australia, Germany, and Belgium retain mid-range ratings, while Singapore and Canada perform at the top. Despite having lower scores, Brazil and Peru have shown some development.

Limitations

Although the Learningtower package makes it easier to access the PISA dataset, it has drawbacks, including less customisation options for more complex analyses, performance problems with huge datasets, and possible incompatibilities with other R versions. Furthermore, for more complicated use scenarios, the documentation might not be adequate. Inherent limitations of the PISA dataset include the fact that it is cross-sectional, which precludes longitudinal tracking, the possibility of sample biases, and the difficulties caused by linguistic and cultural differences that could compromise the comparability of results. Furthermore, the depth of analysis may be constrained by incomplete or missing data, a lack of socioeconomic indicators, and out-of-date background questionnaires. Lastly, the emphasis on standardised test scores may obscure more important educational objectives that the tests do not measure, such creativity and critical thinking.

Thank You